Application of Generalized Predictive Control to a Fractional Order Plant

2007 ◽  
Author(s):  
Miguel Romero ◽  
A´ngel P. de Madrid ◽  
Carolina Man˜oso ◽  
Roberto Herna´ndez
Keyword(s):  
Fractional Order ◽  
Predictive Control ◽  
Large Scale ◽  
Generalized Predictive Control ◽  
Discrete Approximations ◽  
Stability Performance ◽  
Industry Standard ◽  
Constraints Handling ◽  
Model Predictive Controllers ◽  
Predictive Controllers

This paper deals with the use of model predictive controllers (MPC) for controlling fractional order plants. MPC is an industry standard due to its intrinsic ability to handle input and state constraints for large scale multivariable plants. The method is illustrated with Generalized Predictive Control (GPC) and two low order discrete approximations of the fractional order plant (the so-called Chebyshev-Pade´ and Rational Chebyshev approximations) as model. It is shown how stability, performance and constraints handling can be achieved with ease when dealing with fractional order plants. It is also shown how robustness can be improved by means of a prefilter.

scholarly journals Time-Varying Scheme for Noncentralized Model Predictive Control of Large-Scale Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Alfredo Núñez ◽  
Carlos Ocampo-Martinez ◽  
José María Maestre ◽  
Bart De Schutter
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Large Scale ◽  
Reduction Methods ◽  
Predictive Controller ◽  
Online Methods ◽  
Water Tanks ◽  
Predictive Controllers ◽  
Decentralized Model

The noncentralized model predictive control (NC-MPC) framework in this paper refers to any distributed, hierarchical, or decentralized model predictive controller (or a combination of them) the structure of which can change over time and the control actions of which are not obtained based on a centralized computation. Within this framework, we propose suitable online methods to decide which information is shared and how this information is used between the different local predictive controllers operating in a decentralized, distributed, and/or hierarchical way. Evaluating all the possible structures of the NC-MPC controller leads to a combinatorial optimization problem. Therefore, we also propose heuristic reduction methods, to keep the number of NC-MPC problems tractable to be solved. To show the benefits of the proposed framework, a case study of a set of coupled water tanks is presented.

Generalized predictive control for fractional order dynamic model of solid oxide fuel cell output power

2010 ◽  
Vol 195 (24) ◽  
pp. 8097-8103 ◽  
Author(s):  
Zhonghua Deng ◽  
Hongliang Cao ◽  
Xi Li ◽  
Jianhua Jiang ◽  
Jie Yang ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Dynamic Model ◽  
Output Power ◽  
Fractional Order ◽  
Predictive Control ◽  
Solid Oxide ◽  
Generalized Predictive Control ◽  
Oxide Fuel

scholarly journals A Comparative Study of Stochastic Model Predictive Controllers

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2078
Author(s):  
Edwin González ◽  
Javier Sanchis ◽  
Sergio García-Nieto ◽  
José Salcedo
Keyword(s):  
Model Predictive Control ◽  
Stochastic Model ◽  
Comparative Study ◽  
Linear Systems ◽  
Predictive Control ◽  
Probabilistic Constraints ◽  
Numerical Examples ◽  
Model Predictive Controllers ◽  
Predictive Controllers ◽  
Stochastic Model Predictive Control

A comparative study of two state-of-the-art stochastic model predictive controllers for linear systems with parametric and additive uncertainties is presented. On the one hand, Stochastic Model Predictive Control (SMPC) is based on analytical methods and solves an optimal control problem (OCP) similar to a classic Model Predictive Control (MPC) with constraints. SMPC defines probabilistic constraints on the states, which are transformed into equivalent deterministic ones. On the other hand, Scenario-based Model Predictive Control (SCMPC) solves an OCP for a specified number of random realizations of uncertainties, also called scenarios. In this paper, Classic MPC, SMPC and SCMPC are compared through two numerical examples. Thanks to several Monte-Carlo simulations, performances of classic MPC, SMPC and SCMPC are compared using several criteria, such as number of successful runs, number of times the constraints are violated, integral absolute error and computational cost. Moreover, a Stochastic Model Predictive Control Toolbox was developed by the authors, available on MATLAB Central, in which it is possible to simulate a SMPC or a SCMPC to control multivariable linear systems with additive disturbances. This software was used to carry out part of the simulations of the numerical examples in this article and it can be used for results reproduction.

scholarly journals Distributed Model Predictive Control Applied to a Sewer System

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1595
Author(s):  
Antonio Cembellín ◽  
Mario Francisco ◽  
Pastora Vega
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
System Performance ◽  
Treatment Plant ◽  
Distributed Model ◽  
Sewer System ◽  
Distributed Model Predictive Control ◽  
Model Predictive Controllers ◽  
Predictive Controllers ◽  
Decentralized Model

In this work, a Distributed Model Predictive Control (MPC) methodology with fuzzy negotiation among subsystems has been developed and applied to a simulated sewer network. The wastewater treatment plant (WWTP) receiving this wastewater has also been considered in the methodology by means of an additional objective for the problem. In order to decompose the system into interconnected local subsystems, sectorization techniques have been applied based on structural analysis. In addition, a dynamic setpoint generation method has been added to improve system performance. The results obtained with the proposed methodology are compared to those obtained with standard centralized and decentralized model predictive controllers.

scholarly journals Coordinating multiple model predictive controllers for the management of large-scale water systems

2012 ◽  
Vol 15 (2) ◽  
pp. 293-305 ◽  
Author(s):  
Abhay Anand ◽  
Stefano Galelli ◽  
Lakshminarayanan Samavedham ◽  
Sitanandam Sundaramoorthy
Keyword(s):  
Large Scale ◽  
Numerical Study ◽  
Water Reservoir ◽  
Multiple Model ◽  
Reservoir Water ◽  
Systems Model ◽  
Tank System ◽  
Decentralized Controllers ◽  
Model Predictive Controllers ◽  
Predictive Controllers

The optimal management of multi-purpose water reservoir networks is a challenging control problem, because of the simultaneous presence of multiple objectives, the uncertainties associated with the inflow processes and the several interactions between the subsystems. For such systems, model predictive control (MPC) is an attractive control strategy that can be implemented in both centralized and decentralized configurations. The latter is easy to implement and is characterized by reduced computational requirements, but its performance is sub-optimum. However, individual decentralized controllers can be coordinated and driven towards the performance of a centralized configuration. Coordination can be achieved through the communication of information between the subsystems, and the modification of the local control problems to ensure cooperation between the controllers. In this work the applicability of coordination algorithms for the operation of water reservoir networks is evaluated. The performance of the algorithms is evaluated through numerical simulation experiments on a quadruple tank system and a two reservoir water network. The analysis also includes a numerical study of the trade-off between the algorithms' computational burden and the different levels of cooperation. The results show the potential of the proposed approach, which could provide a viable alternative to traditional control methods in real-world applications.

scholarly journals The Potential of Fractional Order Distributed MPC Applied to Steam/Water Loop in Large Scale Ships

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 451 ◽  
Author(s):  
Shiquan Zhao ◽  
Ricardo Cajo ◽  
Robain De De Keyser ◽  
Clara-Mihaela Ionescu
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Fractional Order ◽  
Predictive Control ◽  
Large Scale ◽  
Adaptive Controller ◽  
Control Performance ◽  
Order Model ◽  
Fractional Order Model ◽  
Load Fluctuation

The steam/water loop is a crucial part of a steam power plant. However, satisfying control performance is difficult to obtain due to the frequent disturbance and load fluctuation. A fractional order model predictive control was studied in this paper to improve the control performance of the steam/water loop. Firstly, the dynamic of the steam/water loop was introduced in large-scale ships. Then, the model predictive control with an extended prediction self adaptive controller framework was designed for the steam/water loop with a distributed scheme. Instead of an integer cost function, a fractional order cost function was applied in the model predictive control optimization step. The superiority of the fractional order model predictive control was validated with reference tracking and load fluctuation experiments.

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